Conclusion to 'Market Risk Modelling (2nd edition)'
Preface
Risk Modelling and its Myths
Mastering the R Statistical Package
Key Concepts on Probability
Tools for Describing Risk Factors and Portfolios
The Essentials of Hypothesis Testing for Risk Managers
Alternative Methods to Measure Correlation
A Primer On Maximum Likelihood Estimation
Regression in a Nutshell
Fitting Probability Distributions to Data
Practical Principal Components Analysis
Three Essential Models for Volatility
Random Numbers and Applied Simulation
Tail Risk Modelling
Conclusion to 'Market Risk Modelling (2nd edition)'
This book has described some of the proven statistical ideas inherent in risk modelling, ideas that can simultaneously give you a laser-like focus as well as generate deeper insight into the relationship between risk and reward. They can be used to protect value and generate extraordinary profit. You have also discovered the power of the R statistical package. Powered by the tutorial in Chapter 2, you have become competent in its use, and have worked through the numerous examples littered throughout this text. Understanding these quantitative ideas is why you bought this book. Indeed, it is the sole purpose of this text. However, at the outset, having sold very many copies of my books and spoken to many successful readers, I want to stress a very important observation: what you get from this text will not depend solely on what is written in these pages.
Yes, you can read, fully digest and use these methods straight out of the box. But to achieve the maximum benefit, you must develop a broad understanding of risk management and particularly a very robust notion of what risk actually is. Such an understanding can take many years to acquire. The successful risk manager is crystal
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